FigureS3_eto

Load Data

dataset <- read.delim("raw_data/FigureS3_eto.txt", stringsAsFactors = FALSE)

dataset$genotype <- gsub(" ", "", dataset$genotype )
dataset$genotype <- factor(dataset$genotype)
dataset$Experiment <- factor(rep(paste0("exp", 1:(length(dataset$genotype)/length(levels(dataset$genotype)))), each=length(unique(dataset$genotype))))

dataset$siRNA <-  factor(gsub(".*[T,O]\\+","",dataset$genotype))
dataset$genotype <-  factor(gsub("\\+.*","",dataset$genotype))

dataset$UID <- factor(paste(dataset$Experiment, dataset$genotype, dataset$siRNA))
dataset$GSID <- factor(paste(dataset$genotype, dataset$siRNA))

# wide format
kable(dataset, row.names = F)

genotype	NT	etoposide_5uM	etoposide_10uM	etoposide_25uM	Experiment	siRNA	UID	GSID
WT	1434	991	685	222	exp1	siCtrl	exp1 WT siCtrl	WT siCtrl
WT	1191	555	43	29	exp1	siPARP1	exp1 WT siPARP1	WT siPARP1
ALC1KO	1409	631	11	4	exp1	siCtrl	exp1 ALC1KO siCtrl	ALC1KO siCtrl
ALC1KO	1199	523	7	4	exp1	siPARP1	exp1 ALC1KO siPARP1	ALC1KO siPARP1
WT	2704	1555	1088	596	exp2	siCtrl	exp2 WT siCtrl	WT siCtrl
WT	1902	656	560	21	exp2	siPARP1	exp2 WT siPARP1	WT siPARP1
ALC1KO	1912	672	592	59	exp2	siCtrl	exp2 ALC1KO siCtrl	ALC1KO siCtrl
ALC1KO	3200	1010	976	10	exp2	siPARP1	exp2 ALC1KO siPARP1	ALC1KO siPARP1
WT	2845	1288	991	566	exp3	siCtrl	exp3 WT siCtrl	WT siCtrl
WT	1805	722	494	2	exp3	siPARP1	exp3 WT siPARP1	WT siPARP1
ALC1KO	1912	886	528	14	exp3	siCtrl	exp3 ALC1KO siCtrl	ALC1KO siCtrl
ALC1KO	2220	1220	545	4	exp3	siPARP1	exp3 ALC1KO siPARP1	ALC1KO siPARP1

library(reshape2)
# reshape to long format
dataset <- melt(dataset, variable.name = "Treatment", value.name = "Counts")

dataset$genotype <- relevel(dataset$genotype, ref = "WT")
dataset$siRNA <- relevel(dataset$siRNA, ref = "siCtrl")
dataset$UID <- relevel(dataset$UID, ref = "exp1 WT siCtrl")

dataset$Etoposide <- gsub("NT","1",dataset$Treatment)
dataset$Etoposide <- gsub("etoposide_|uM","",dataset$Etoposide)
dataset$Etoposide <- log10(as.integer(dataset$Etoposide))




dataset$Offset <- NA
for(uid in levels(dataset$UID)){
        dataset$Offset[dataset$UID == uid] <- mean(dataset$Counts[dataset$UID == uid])
}

dataset$NormCounts <- dataset$Counts / dataset$Offset



dataset$Offset2 <- NA
for(gsid in levels(dataset$GSID)){
        dataset$Offset2[dataset$GSID == gsid] <- mean(dataset$NormCounts[dataset$GSID == gsid & dataset$Etoposide == 0])
}

dataset$NormCounts2 <- dataset$NormCounts / dataset$Offset2



# long format
kable(dataset, row.names = F)

genotype	Experiment	siRNA	UID	GSID	Treatment	Counts	Etoposide	Offset	NormCounts	Offset2	NormCounts2
WT	exp1	siCtrl	exp1 WT siCtrl	WT siCtrl	NT	1434	0.00000	833.00	1.7214886	1.847148	0.9319710
WT	exp1	siPARP1	exp1 WT siPARP1	WT siPARP1	NT	1191	0.00000	454.50	2.6204620	2.477507	1.0577013
ALC1KO	exp1	siCtrl	exp1 ALC1KO siCtrl	ALC1KO siCtrl	NT	1409	0.00000	513.75	2.7425791	2.465514	1.1123762
ALC1KO	exp1	siPARP1	exp1 ALC1KO siPARP1	ALC1KO siPARP1	NT	1199	0.00000	433.25	2.7674553	2.485670	1.1133638
WT	exp2	siCtrl	exp2 WT siCtrl	WT siCtrl	NT	2704	0.00000	1485.75	1.8199563	1.847148	0.9852789
WT	exp2	siPARP1	exp2 WT siPARP1	WT siPARP1	NT	1902	0.00000	784.75	2.4237018	2.477507	0.9782827
ALC1KO	exp2	siCtrl	exp2 ALC1KO siCtrl	ALC1KO siCtrl	NT	1912	0.00000	808.75	2.3641422	2.465514	0.9588842
ALC1KO	exp2	siPARP1	exp2 ALC1KO siPARP1	ALC1KO siPARP1	NT	3200	0.00000	1299.00	2.4634334	2.485670	0.9910540
WT	exp3	siCtrl	exp3 WT siCtrl	WT siCtrl	NT	2845	0.00000	1422.50	2.0000000	1.847148	1.0827501
WT	exp3	siPARP1	exp3 WT siPARP1	WT siPARP1	NT	1805	0.00000	755.75	2.3883559	2.477507	0.9640160
ALC1KO	exp3	siCtrl	exp3 ALC1KO siCtrl	ALC1KO siCtrl	NT	1912	0.00000	835.00	2.2898204	2.465514	0.9287396
ALC1KO	exp3	siPARP1	exp3 ALC1KO siPARP1	ALC1KO siPARP1	NT	2220	0.00000	997.25	2.2261218	2.485670	0.8955821
WT	exp1	siCtrl	exp1 WT siCtrl	WT siCtrl	etoposide_5uM	991	0.69897	833.00	1.1896759	1.847148	0.6440608
WT	exp1	siPARP1	exp1 WT siPARP1	WT siPARP1	etoposide_5uM	555	0.69897	454.50	1.2211221	2.477507	0.4928835
ALC1KO	exp1	siCtrl	exp1 ALC1KO siCtrl	ALC1KO siCtrl	etoposide_5uM	631	0.69897	513.75	1.2282238	2.465514	0.4981614
ALC1KO	exp1	siPARP1	exp1 ALC1KO siPARP1	ALC1KO siPARP1	etoposide_5uM	523	0.69897	433.25	1.2071552	2.485670	0.4856458
WT	exp2	siCtrl	exp2 WT siCtrl	WT siCtrl	etoposide_5uM	1555	0.69897	1485.75	1.0466095	1.847148	0.5666083
WT	exp2	siPARP1	exp2 WT siPARP1	WT siPARP1	etoposide_5uM	656	0.69897	784.75	0.8359350	2.477507	0.3374098
ALC1KO	exp2	siCtrl	exp2 ALC1KO siCtrl	ALC1KO siCtrl	etoposide_5uM	672	0.69897	808.75	0.8309119	2.465514	0.3370137
ALC1KO	exp2	siPARP1	exp2 ALC1KO siPARP1	ALC1KO siPARP1	etoposide_5uM	1010	0.69897	1299.00	0.7775212	2.485670	0.3128014
WT	exp3	siCtrl	exp3 WT siCtrl	WT siCtrl	etoposide_5uM	1288	0.69897	1422.50	0.9054482	1.847148	0.4901870
WT	exp3	siPARP1	exp3 WT siPARP1	WT siPARP1	etoposide_5uM	722	0.69897	755.75	0.9553424	2.477507	0.3856064
ALC1KO	exp3	siCtrl	exp3 ALC1KO siCtrl	ALC1KO siCtrl	etoposide_5uM	886	0.69897	835.00	1.0610778	2.465514	0.4303678
ALC1KO	exp3	siPARP1	exp3 ALC1KO siPARP1	ALC1KO siPARP1	etoposide_5uM	1220	0.69897	997.25	1.2233643	2.485670	0.4921668
WT	exp1	siCtrl	exp1 WT siCtrl	WT siCtrl	etoposide_10uM	685	1.00000	833.00	0.8223289	1.847148	0.4451884
WT	exp1	siPARP1	exp1 WT siPARP1	WT siPARP1	etoposide_10uM	43	1.00000	454.50	0.0946095	2.477507	0.0381874
ALC1KO	exp1	siCtrl	exp1 ALC1KO siCtrl	ALC1KO siCtrl	etoposide_10uM	11	1.00000	513.75	0.0214112	2.465514	0.0086843
ALC1KO	exp1	siPARP1	exp1 ALC1KO siPARP1	ALC1KO siPARP1	etoposide_10uM	7	1.00000	433.25	0.0161570	2.485670	0.0065000
WT	exp2	siCtrl	exp2 WT siCtrl	WT siCtrl	etoposide_10uM	1088	1.00000	1485.75	0.7322901	1.847148	0.3964436
WT	exp2	siPARP1	exp2 WT siPARP1	WT siPARP1	etoposide_10uM	560	1.00000	784.75	0.7136031	2.477507	0.2880328
ALC1KO	exp2	siCtrl	exp2 ALC1KO siCtrl	ALC1KO siCtrl	etoposide_10uM	592	1.00000	808.75	0.7319938	2.465514	0.2968930
ALC1KO	exp2	siPARP1	exp2 ALC1KO siPARP1	ALC1KO siPARP1	etoposide_10uM	976	1.00000	1299.00	0.7513472	2.485670	0.3022715
WT	exp3	siCtrl	exp3 WT siCtrl	WT siCtrl	etoposide_10uM	991	1.00000	1422.50	0.6966608	1.847148	0.3771548
WT	exp3	siPARP1	exp3 WT siPARP1	WT siPARP1	etoposide_10uM	494	1.00000	755.75	0.6536553	2.477507	0.2638360
ALC1KO	exp3	siCtrl	exp3 ALC1KO siCtrl	ALC1KO siCtrl	etoposide_10uM	528	1.00000	835.00	0.6323353	2.465514	0.2564720
ALC1KO	exp3	siPARP1	exp3 ALC1KO siPARP1	ALC1KO siPARP1	etoposide_10uM	545	1.00000	997.25	0.5465029	2.485670	0.2198614
WT	exp1	siCtrl	exp1 WT siCtrl	WT siCtrl	etoposide_25uM	222	1.39794	833.00	0.2665066	1.847148	0.1442800
WT	exp1	siPARP1	exp1 WT siPARP1	WT siPARP1	etoposide_25uM	29	1.39794	454.50	0.0638064	2.477507	0.0257543
ALC1KO	exp1	siCtrl	exp1 ALC1KO siCtrl	ALC1KO siCtrl	etoposide_25uM	4	1.39794	513.75	0.0077859	2.465514	0.0031579
ALC1KO	exp1	siPARP1	exp1 ALC1KO siPARP1	ALC1KO siPARP1	etoposide_25uM	4	1.39794	433.25	0.0092325	2.485670	0.0037143
WT	exp2	siCtrl	exp2 WT siCtrl	WT siCtrl	etoposide_25uM	596	1.39794	1485.75	0.4011442	1.847148	0.2171695
WT	exp2	siPARP1	exp2 WT siPARP1	WT siPARP1	etoposide_25uM	21	1.39794	784.75	0.0267601	2.477507	0.0108012
ALC1KO	exp2	siCtrl	exp2 ALC1KO siCtrl	ALC1KO siCtrl	etoposide_25uM	59	1.39794	808.75	0.0729521	2.465514	0.0295890
ALC1KO	exp2	siPARP1	exp2 ALC1KO siPARP1	ALC1KO siPARP1	etoposide_25uM	10	1.39794	1299.00	0.0076982	2.485670	0.0030970
WT	exp3	siCtrl	exp3 WT siCtrl	WT siCtrl	etoposide_25uM	566	1.39794	1422.50	0.3978910	1.847148	0.2154083
WT	exp3	siPARP1	exp3 WT siPARP1	WT siPARP1	etoposide_25uM	2	1.39794	755.75	0.0026464	2.477507	0.0010682
ALC1KO	exp3	siCtrl	exp3 ALC1KO siCtrl	ALC1KO siCtrl	etoposide_25uM	14	1.39794	835.00	0.0167665	2.465514	0.0068004
ALC1KO	exp3	siPARP1	exp3 ALC1KO siPARP1	ALC1KO siPARP1	etoposide_25uM	4	1.39794	997.25	0.0040110	2.485670	0.0016137

Plot Data

library(ggplot2)

# raw data
ggplot(dataset, aes(x=Etoposide, y=Counts)) + 
        theme_bw() +
        theme(panel.grid=element_blank(), text = element_text(size=14)) +
        geom_smooth(method=lm, formula = y ~ poly(x,2), se=FALSE, aes(colour=siRNA)) +
        geom_point(aes(colour=siRNA, shape=Experiment), size=2) +        
        facet_grid(. ~ genotype) +
        xlab(label = "Etoposide (log10 µM)") +
        scale_shape_manual(values=15:20) +
        scale_color_manual(values=c("#000000","#FF0000"))

# NormCounts Linear
ggplot(dataset, aes(x=Etoposide, y=NormCounts, color=siRNA)) + 
        theme_bw() +
        theme(panel.grid=element_blank(), text = element_text(size=14)) +
        geom_point(aes(colour=siRNA), size=2) +        
        geom_smooth(method=lm, formula = y ~ x, se=FALSE) +
        facet_grid(. ~ genotype) +
        xlab(label = "Etoposide (log10 µM)") +
        scale_color_manual(values=c("#000000","#FF0000"))

# NormCounts2 Linear
ggplot(dataset, aes(x=Etoposide, y=NormCounts2, color=siRNA)) + 
        theme_bw() +
        theme(panel.grid=element_blank(), text = element_text(size=14)) +
        geom_point(aes(colour=siRNA), size=2) +        
        geom_smooth(method=lm, formula = y ~ x, se=FALSE) +
        facet_grid(. ~ genotype) +
        xlab(label = "Etoposide (log10 µM)") +
        scale_color_manual(values=c("#000000","#FF0000"))

# NormCounts Quadratic
ggplot(dataset, aes(x=Etoposide, y=NormCounts, color=siRNA)) + 
        theme_bw() +
        theme(panel.grid=element_blank(), text = element_text(size=14)) +
        geom_point(aes(colour=siRNA), size=2) +        
        geom_smooth(method=lm, formula = y ~ poly(x,2), se=FALSE) +
        facet_grid(. ~ genotype) +
        xlab(label = "Etoposide (log10 µM)")+
        scale_color_manual(values=c("#000000","#FF0000"))

# NormCounts2 Quadratic
ggplot(dataset, aes(x=Etoposide, y=NormCounts2, color=siRNA)) + 
        theme_bw() +
        theme(panel.grid=element_blank(), text = element_text(size=14)) +
        geom_point(aes(colour=siRNA), size=2) +        
        geom_smooth(method=lm, formula = y ~ poly(x,2), se=FALSE) +
        facet_grid(. ~ genotype) +
        xlab(label = "Etoposide (log10 µM)") +
        scale_color_manual(values=c("#000000","#FF0000"))

# NormCounts Cubic
ggplot(dataset, aes(x=Etoposide, y=NormCounts, color=siRNA)) + 
        theme_bw() +
        theme(panel.grid=element_blank(), text = element_text(size=14)) +
        geom_point(aes(colour=siRNA), size=2) +        
        geom_smooth(method=lm, formula = y ~ poly(x,3), se=FALSE) +
        facet_grid(. ~ genotype) +
        xlab(label = "Etoposide (log10 µM)")+
        scale_color_manual(values=c("#000000","#FF0000"))

# NormCounts2 Cubic
ggplot(dataset, aes(x=Etoposide, y=NormCounts2, color=siRNA)) + 
        theme_bw() +
        theme(panel.grid=element_blank(), text = element_text(size=14)) +
        geom_point(aes(colour=siRNA), size=2) +        
        geom_smooth(method=lm, formula = y ~ poly(x,3), se=FALSE) +
        facet_grid(. ~ genotype) +
        xlab(label = "Etoposide (log10 µM)") +
        scale_color_manual(values=c("#000000","#FF0000"))

library(Cairo)


cairo_pdf("FigureS3_eto.pdf", width = 5, height = 4, family = "Arial")

ggplot(dataset, aes(x=Etoposide, y=NormCounts2)) + 
        theme_bw() +
        theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank(), 
              axis.line = element_line(colour = "black"), text = element_text(size=14),
              panel.border = element_blank(), panel.background = element_blank()) +
        geom_point(aes(colour = siRNA, shape = genotype), size=1.75) +
        geom_smooth(method=lm, formula = y ~ poly(x,2), se=TRUE, 
                    aes(group = GSID,colour = siRNA, linetype = genotype), fill='#DDDDDD', size=0.5) +
        #facet_grid(. ~ genotype) +
        xlab(label = "Etoposide (log10 µM)") +
        ylab(label = "Normalized Counts") +
        scale_color_manual(values=c("#000000","#FF0000")) +
        guides(linetype = guide_legend(override.aes= list(color = "#555555"))) 

dev.off()

## quartz_off_screen 
##                 2

Models

library(MASS)
library(DHARMa)
library(lme4)
library(lmerTest)
library(bbmle)

Linear formula

fit1 <- lm(Counts ~ Experiment + Etoposide*siRNA*genotype, data = dataset)
print(summary(fit1))

## 
## Call:
## lm(formula = Counts ~ Experiment + Etoposide * siRNA * genotype, 
##     data = dataset)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -612.87 -104.64  -32.67  111.77  852.19 
## 
## Coefficients:
##                                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                            1959.50     151.73  12.915 1.79e-15 ***
## Experimentexp2                          535.94      95.40   5.618 1.90e-06 ***
## Experimentexp3                          444.00      95.40   4.654 3.89e-05 ***
## Etoposide                             -1342.07     152.39  -8.807 1.03e-10 ***
## siRNAsiPARP1                           -717.95     199.94  -3.591 0.000931 ***
## genotypeALC1KO                         -600.72     199.94  -3.005 0.004690 ** 
## Etoposide:siRNAsiPARP1                  175.48     215.51   0.814 0.420572    
## Etoposide:genotypeALC1KO                 94.03     215.51   0.436 0.665071    
## siRNAsiPARP1:genotypeALC1KO            1171.04     282.76   4.141 0.000185 ***
## Etoposide:siRNAsiPARP1:genotypeALC1KO  -514.43     304.78  -1.688 0.099627 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 269.8 on 38 degrees of freedom
## Multiple R-squared:  0.9095, Adjusted R-squared:  0.8881 
## F-statistic: 42.44 on 9 and 38 DF,  p-value: < 2.2e-16

cat("AIC: ", AIC(fit1))

## AIC:  684.3934

simres <- simulateResiduals(fittedModel = fit1)
plot(simres)

fit2 <- lm(NormCounts ~ Etoposide*siRNA*genotype, data = dataset)
print(summary(fit2))

## 
## Call:
## lm(formula = NormCounts ~ Etoposide * siRNA * genotype, data = dataset)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.57621 -0.06797  0.06013  0.12987  0.36272 
## 
## Coefficients:
##                                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                             1.8282     0.1175  15.554  < 2e-16 ***
## Etoposide                              -1.0697     0.1267  -8.443    2e-10 ***
## siRNAsiPARP1                            0.5529     0.1662   3.326 0.001894 ** 
## genotypeALC1KO                          0.5516     0.1662   3.319 0.001936 ** 
## Etoposide:siRNAsiPARP1                 -0.7142     0.1792  -3.986 0.000278 ***
## Etoposide:genotypeALC1KO               -0.7125     0.1792  -3.977 0.000285 ***
## siRNAsiPARP1:genotypeALC1KO            -0.5239     0.2351  -2.229 0.031515 *  
## Etoposide:siRNAsiPARP1:genotypeALC1KO   0.6767     0.2534   2.671 0.010892 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2243 on 40 degrees of freedom
## Multiple R-squared:  0.944,  Adjusted R-squared:  0.9341 
## F-statistic: 96.24 on 7 and 40 DF,  p-value: < 2.2e-16

cat("AIC: ", AIC(fit2))

## AIC:  1.984

simres <- simulateResiduals(fittedModel = fit2)
plot(simres)

fit3 <- lm(NormCounts2 ~ Etoposide*siRNA*genotype, data = dataset)
print(summary(fit3))

## 
## Call:
## lm(formula = NormCounts2 ~ Etoposide * siRNA * genotype, data = dataset)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.23371 -0.03407  0.02551  0.05485  0.14712 
## 
## Coefficients:
##                                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                            0.98976    0.04846  20.426  < 2e-16 ***
## Etoposide                             -0.57913    0.05223 -11.088 9.04e-14 ***
## siRNAsiPARP1                          -0.02865    0.06853  -0.418   0.6781    
## genotypeALC1KO                        -0.02450    0.06853  -0.357   0.7226    
## Etoposide:siRNAsiPARP1                -0.14091    0.07386  -1.908   0.0636 .  
## Etoposide:genotypeALC1KO              -0.14373    0.07386  -1.946   0.0587 .  
## siRNAsiPARP1:genotypeALC1KO            0.03248    0.09691   0.335   0.7392    
## Etoposide:siRNAsiPARP1:genotypeALC1KO  0.13171    0.10446   1.261   0.2147    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.09248 on 40 degrees of freedom
## Multiple R-squared:  0.9474, Adjusted R-squared:  0.9382 
## F-statistic:   103 on 7 and 40 DF,  p-value: < 2.2e-16

cat("AIC: ", AIC(fit3))

## AIC:  -83.08461

simres <- simulateResiduals(fittedModel = fit3)
plot(simres)

fit4 <- lmer(Counts ~ Etoposide*siRNA*genotype + (1|UID), data = dataset)
print(summary(fit4))

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Counts ~ Etoposide * siRNA * genotype + (1 | UID)
##    Data: dataset
## 
## REML criterion at convergence: 588.7
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.05873 -0.41520 -0.03368  0.41184  2.78089 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  UID      (Intercept) 79092    281.2   
##  Residual             71604    267.6   
## Number of obs: 48, groups:  UID, 12
## 
## Fixed effects:
##                                       Estimate Std. Error       df t value
## (Intercept)                            2286.15     214.53    15.51  10.657
## Etoposide                             -1342.07     151.12    32.00  -8.881
## siRNAsiPARP1                           -717.95     303.38    15.51  -2.366
## genotypeALC1KO                         -600.72     303.38    15.51  -1.980
## Etoposide:siRNAsiPARP1                  175.48     213.72    32.00   0.821
## Etoposide:genotypeALC1KO                 94.03     213.72    32.00   0.440
## siRNAsiPARP1:genotypeALC1KO            1171.04     429.05    15.51   2.729
## Etoposide:siRNAsiPARP1:genotypeALC1KO  -514.43     302.25    32.00  -1.702
##                                       Pr(>|t|)    
## (Intercept)                           1.54e-08 ***
## Etoposide                             3.81e-10 ***
## siRNAsiPARP1                            0.0313 *  
## genotypeALC1KO                          0.0657 .  
## Etoposide:siRNAsiPARP1                  0.4177    
## Etoposide:genotypeALC1KO                0.6629    
## siRNAsiPARP1:genotypeALC1KO             0.0152 *  
## Etoposide:siRNAsiPARP1:genotypeALC1KO   0.0984 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Etopsd sRNAsPARP1 gALC1K Et:RNAPARP1 E:ALC1 sRNAPARP1:
## Etoposide   -0.545                                                       
## siRNAsPARP1 -0.707  0.386                                                
## gntypALC1KO -0.707  0.386  0.500                                         
## Et:RNAPARP1  0.386 -0.707 -0.545     -0.273                              
## Etps:ALC1KO  0.386 -0.707 -0.273     -0.545  0.500                       
## sRNAPARP1:A  0.500 -0.273 -0.707     -0.707  0.386       0.386           
## E:RNAPARP1: -0.273  0.500  0.386      0.386 -0.707      -0.707 -0.545

cat("AIC: ", AIC(fit4))

## AIC:  608.7001

simres <- simulateResiduals(fittedModel = fit4)
plot(simres)

Quadratic formula

fit5 <- lm(Counts ~ Experiment + poly(Etoposide,2)*siRNA*genotype, data = dataset)
print(summary(fit5))

## 
## Call:
## lm(formula = Counts ~ Experiment + poly(Etoposide, 2) * siRNA * 
##     genotype, data = dataset)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -678.58 -119.82  -13.13   67.68  786.48 
## 
## Coefficients:
##                                                 Estimate Std. Error t value
## (Intercept)                                       920.44      96.42   9.546
## Experimentexp2                                    535.94      96.42   5.558
## Experimentexp3                                    444.00      96.42   4.605
## poly(Etoposide, 2)1                             -4752.70     545.46  -8.713
## poly(Etoposide, 2)2                               337.13     545.46   0.618
## siRNAsiPARP1                                     -582.08     111.34  -5.228
## genotypeALC1KO                                   -527.92     111.34  -4.741
## poly(Etoposide, 2)1:siRNAsiPARP1                  621.44     771.39   0.806
## poly(Etoposide, 2)2:siRNAsiPARP1                  186.92     771.39   0.242
## poly(Etoposide, 2)1:genotypeALC1KO                333.00     771.39   0.432
## poly(Etoposide, 2)2:genotypeALC1KO                167.42     771.39   0.217
## siRNAsiPARP1:genotypeALC1KO                       772.75     157.46   4.908
## poly(Etoposide, 2)1:siRNAsiPARP1:genotypeALC1KO -1821.77    1090.91  -1.670
## poly(Etoposide, 2)2:siRNAsiPARP1:genotypeALC1KO  -136.10    1090.91  -0.125
##                                                 Pr(>|t|)    
## (Intercept)                                     3.78e-11 ***
## Experimentexp2                                  3.23e-06 ***
## Experimentexp3                                  5.56e-05 ***
## poly(Etoposide, 2)1                             3.51e-10 ***
## poly(Etoposide, 2)2                                0.541    
## siRNAsiPARP1                                    8.69e-06 ***
## genotypeALC1KO                                  3.71e-05 ***
## poly(Etoposide, 2)1:siRNAsiPARP1                   0.426    
## poly(Etoposide, 2)2:siRNAsiPARP1                   0.810    
## poly(Etoposide, 2)1:genotypeALC1KO                 0.669    
## poly(Etoposide, 2)2:genotypeALC1KO                 0.829    
## siRNAsiPARP1:genotypeALC1KO                     2.26e-05 ***
## poly(Etoposide, 2)1:siRNAsiPARP1:genotypeALC1KO    0.104    
## poly(Etoposide, 2)2:siRNAsiPARP1:genotypeALC1KO    0.901    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 272.7 on 34 degrees of freedom
## Multiple R-squared:  0.9173, Adjusted R-squared:  0.8857 
## F-statistic: 29.01 on 13 and 34 DF,  p-value: 1.325e-14

cat("AIC: ", AIC(fit5))

## AIC:  688.0792

simres <- simulateResiduals(fittedModel = fit5)
plot(simres)

fit6 <- lm(NormCounts ~ poly(Etoposide,2)*siRNA*genotype, data = dataset)
print(summary(fit6))

## 
## Call:
## lm(formula = NormCounts ~ poly(Etoposide, 2) * siRNA * genotype, 
##     data = dataset)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.49539 -0.09131  0.01085  0.13695  0.27277 
## 
## Coefficients:
##                                                   Estimate Std. Error t value
## (Intercept)                                      1.000e+00  5.989e-02  16.697
## poly(Etoposide, 2)1                             -3.788e+00  4.149e-01  -9.130
## poly(Etoposide, 2)2                              1.515e-01  4.149e-01   0.365
## siRNAsiPARP1                                    -1.894e-17  8.470e-02   0.000
## genotypeALC1KO                                  -3.408e-16  8.470e-02   0.000
## poly(Etoposide, 2)1:siRNAsiPARP1                -2.529e+00  5.868e-01  -4.310
## poly(Etoposide, 2)2:siRNAsiPARP1                 6.843e-01  5.868e-01   1.166
## poly(Etoposide, 2)1:genotypeALC1KO              -2.523e+00  5.868e-01  -4.300
## poly(Etoposide, 2)2:genotypeALC1KO               6.269e-01  5.868e-01   1.068
## siRNAsiPARP1:genotypeALC1KO                      4.199e-16  1.198e-01   0.000
## poly(Etoposide, 2)1:siRNAsiPARP1:genotypeALC1KO  2.396e+00  8.299e-01   2.888
## poly(Etoposide, 2)2:siRNAsiPARP1:genotypeALC1KO -7.370e-01  8.299e-01  -0.888
##                                                 Pr(>|t|)    
## (Intercept)                                      < 2e-16 ***
## poly(Etoposide, 2)1                             6.67e-11 ***
## poly(Etoposide, 2)2                             0.717151    
## siRNAsiPARP1                                    1.000000    
## genotypeALC1KO                                  1.000000    
## poly(Etoposide, 2)1:siRNAsiPARP1                0.000121 ***
## poly(Etoposide, 2)2:siRNAsiPARP1                0.251248    
## poly(Etoposide, 2)1:genotypeALC1KO              0.000125 ***
## poly(Etoposide, 2)2:genotypeALC1KO              0.292513    
## siRNAsiPARP1:genotypeALC1KO                     1.000000    
## poly(Etoposide, 2)1:siRNAsiPARP1:genotypeALC1KO 0.006526 ** 
## poly(Etoposide, 2)2:siRNAsiPARP1:genotypeALC1KO 0.380373    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2075 on 36 degrees of freedom
## Multiple R-squared:  0.9569, Adjusted R-squared:  0.9437 
## F-statistic: 72.59 on 11 and 36 DF,  p-value: < 2.2e-16

cat("AIC: ", AIC(fit6))

## AIC:  -2.576078

simres <- simulateResiduals(fittedModel = fit6)
plot(simres)

fit7 <- lm(NormCounts2 ~ poly(Etoposide,2)*siRNA*genotype, data = dataset)
print(summary(fit7))

## 
## Call:
## lm(formula = NormCounts2 ~ poly(Etoposide, 2) * siRNA * genotype, 
##     data = dataset)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.199934 -0.038681  0.004367  0.057228  0.109769 
## 
## Coefficients:
##                                                 Estimate Std. Error t value
## (Intercept)                                      0.54138    0.02480  21.827
## poly(Etoposide, 2)1                             -2.05090    0.17184 -11.935
## poly(Etoposide, 2)2                              0.08202    0.17184   0.477
## siRNAsiPARP1                                    -0.13774    0.03508  -3.927
## genotypeALC1KO                                  -0.13578    0.03508  -3.871
## poly(Etoposide, 2)1:siRNAsiPARP1                -0.49900    0.24302  -2.053
## poly(Etoposide, 2)2:siRNAsiPARP1                 0.25533    0.24302   1.051
## poly(Etoposide, 2)1:genotypeALC1KO              -0.50901    0.24302  -2.095
## poly(Etoposide, 2)2:genotypeALC1KO               0.23369    0.24302   0.962
## siRNAsiPARP1:genotypeALC1KO                      0.13445    0.04961   2.710
## poly(Etoposide, 2)1:siRNAsiPARP1:genotypeALC1KO  0.46642    0.34368   1.357
## poly(Etoposide, 2)2:siRNAsiPARP1:genotypeALC1KO -0.27911    0.34368  -0.812
##                                                 Pr(>|t|)    
## (Intercept)                                      < 2e-16 ***
## poly(Etoposide, 2)1                              4.5e-14 ***
## poly(Etoposide, 2)2                             0.636020    
## siRNAsiPARP1                                    0.000373 ***
## genotypeALC1KO                                  0.000439 ***
## poly(Etoposide, 2)1:siRNAsiPARP1                0.047359 *  
## poly(Etoposide, 2)2:siRNAsiPARP1                0.300423    
## poly(Etoposide, 2)1:genotypeALC1KO              0.043315 *  
## poly(Etoposide, 2)2:genotypeALC1KO              0.342667    
## siRNAsiPARP1:genotypeALC1KO                     0.010228 *  
## poly(Etoposide, 2)1:siRNAsiPARP1:genotypeALC1KO 0.183185    
## poly(Etoposide, 2)2:siRNAsiPARP1:genotypeALC1KO 0.422057    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.08592 on 36 degrees of freedom
## Multiple R-squared:  0.9592, Adjusted R-squared:  0.9467 
## F-statistic: 76.87 on 11 and 36 DF,  p-value: < 2.2e-16

cat("AIC: ", AIC(fit7))

## AIC:  -87.20724

simres <- simulateResiduals(fittedModel = fit7)
plot(simres)

fit8 <- lmer(Counts ~ poly(Etoposide,2)*siRNA*genotype + (1|UID), data = dataset)
print(summary(fit8))

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Counts ~ poly(Etoposide, 2) * siRNA * genotype + (1 | UID)
##    Data: dataset
## 
## REML criterion at convergence: 517.6
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.28471 -0.36996 -0.04778  0.38440  2.51161 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  UID      (Intercept) 78658    280.5   
##  Residual             73340    270.8   
## Number of obs: 48, groups:  UID, 12
## 
## Fixed effects:
##                                                 Estimate Std. Error      df
## (Intercept)                                       1247.1      179.8     8.0
## poly(Etoposide, 2)1                              -4752.7      541.6    28.0
## poly(Etoposide, 2)2                                337.1      541.6    28.0
## siRNAsiPARP1                                      -582.1      254.3     8.0
## genotypeALC1KO                                    -527.9      254.3     8.0
## poly(Etoposide, 2)1:siRNAsiPARP1                   621.4      766.0    28.0
## poly(Etoposide, 2)2:siRNAsiPARP1                   186.9      766.0    28.0
## poly(Etoposide, 2)1:genotypeALC1KO                 333.0      766.0    28.0
## poly(Etoposide, 2)2:genotypeALC1KO                 167.4      766.0    28.0
## siRNAsiPARP1:genotypeALC1KO                        772.8      359.6     8.0
## poly(Etoposide, 2)1:siRNAsiPARP1:genotypeALC1KO  -1821.8     1083.3    28.0
## poly(Etoposide, 2)2:siRNAsiPARP1:genotypeALC1KO   -136.1     1083.3    28.0
##                                                 t value Pr(>|t|)    
## (Intercept)                                       6.936  0.00012 ***
## poly(Etoposide, 2)1                              -8.775 1.59e-09 ***
## poly(Etoposide, 2)2                               0.622  0.53868    
## siRNAsiPARP1                                     -2.289  0.05134 .  
## genotypeALC1KO                                   -2.076  0.07155 .  
## poly(Etoposide, 2)1:siRNAsiPARP1                  0.811  0.42403    
## poly(Etoposide, 2)2:siRNAsiPARP1                  0.244  0.80899    
## poly(Etoposide, 2)1:genotypeALC1KO                0.435  0.66708    
## poly(Etoposide, 2)2:genotypeALC1KO                0.219  0.82857    
## siRNAsiPARP1:genotypeALC1KO                       2.149  0.06389 .  
## poly(Etoposide, 2)1:siRNAsiPARP1:genotypeALC1KO  -1.682  0.10374    
## poly(Etoposide, 2)2:siRNAsiPARP1:genotypeALC1KO  -0.126  0.90091    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##                   (Intr) pl(E,2)1 pl(E,2)2 sRNAsPARP1 gALC1K pl(E,2)1:RNAPARP1
## ply(Etp,2)1        0.000                                                      
## ply(Etp,2)2        0.000  0.000                                               
## siRNAsPARP1       -0.707  0.000    0.000                                      
## gntypALC1KO       -0.707  0.000    0.000    0.500                             
## pl(E,2)1:RNAPARP1  0.000 -0.707    0.000    0.000      0.000                  
## pl(E,2)2:RNAPARP1  0.000  0.000   -0.707    0.000      0.000  0.000           
## p(E,2)1:ALC        0.000 -0.707    0.000    0.000      0.000  0.500           
## p(E,2)2:ALC        0.000  0.000   -0.707    0.000      0.000  0.000           
## sRNAPARP1:A        0.500  0.000    0.000   -0.707     -0.707  0.000           
## p(E,2)1:RNAPARP1:  0.000  0.500    0.000    0.000      0.000 -0.707           
## p(E,2)2:RNAPARP1:  0.000  0.000    0.500    0.000      0.000  0.000           
##                   pl(E,2)2:RNAPARP1 p(E,2)1:A p(E,2)2:A sRNAPARP1:
## ply(Etp,2)1                                                       
## ply(Etp,2)2                                                       
## siRNAsPARP1                                                       
## gntypALC1KO                                                       
## pl(E,2)1:RNAPARP1                                                 
## pl(E,2)2:RNAPARP1                                                 
## p(E,2)1:ALC        0.000                                          
## p(E,2)2:ALC        0.500             0.000                        
## sRNAPARP1:A        0.000             0.000     0.000              
## p(E,2)1:RNAPARP1:  0.000            -0.707     0.000     0.000    
## p(E,2)2:RNAPARP1: -0.707             0.000    -0.707     0.000    
##                   p(E,2)1:RNAPARP1:
## ply(Etp,2)1                        
## ply(Etp,2)2                        
## siRNAsPARP1                        
## gntypALC1KO                        
## pl(E,2)1:RNAPARP1                  
## pl(E,2)2:RNAPARP1                  
## p(E,2)1:ALC                        
## p(E,2)2:ALC                        
## sRNAPARP1:A                        
## p(E,2)1:RNAPARP1:                  
## p(E,2)2:RNAPARP1:  0.000

cat("AIC: ", AIC(fit8))

## AIC:  545.6423

simres <- simulateResiduals(fittedModel = fit8)
plot(simres)

Cubic formula

fit9 <- lm(Counts ~ Experiment + poly(Etoposide,3)*siRNA*genotype, data = dataset)
print(summary(fit9))

## 
## Call:
## lm(formula = Counts ~ Experiment + poly(Etoposide, 3) * siRNA * 
##     genotype, data = dataset)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -680.69 -115.51  -13.82   72.61  784.38 
## 
## Coefficients:
##                                                 Estimate Std. Error t value
## (Intercept)                                       920.44     102.55   8.975
## Experimentexp2                                    535.94     102.55   5.226
## Experimentexp3                                    444.00     102.55   4.330
## poly(Etoposide, 3)1                             -4752.70     580.12  -8.193
## poly(Etoposide, 3)2                               337.13     580.12   0.581
## poly(Etoposide, 3)3                               -61.44     580.12  -0.106
## siRNAsiPARP1                                     -582.08     118.42  -4.916
## genotypeALC1KO                                   -527.92     118.42  -4.458
## poly(Etoposide, 3)1:siRNAsiPARP1                  621.44     820.41   0.757
## poly(Etoposide, 3)2:siRNAsiPARP1                  186.92     820.41   0.228
## poly(Etoposide, 3)3:siRNAsiPARP1                  -31.34     820.41  -0.038
## poly(Etoposide, 3)1:genotypeALC1KO                333.00     820.41   0.406
## poly(Etoposide, 3)2:genotypeALC1KO                167.42     820.41   0.204
## poly(Etoposide, 3)3:genotypeALC1KO                 91.05     820.41   0.111
## siRNAsiPARP1:genotypeALC1KO                       772.75     167.47   4.614
## poly(Etoposide, 3)1:siRNAsiPARP1:genotypeALC1KO -1821.77    1160.24  -1.570
## poly(Etoposide, 3)2:siRNAsiPARP1:genotypeALC1KO  -136.10    1160.24  -0.117
## poly(Etoposide, 3)3:siRNAsiPARP1:genotypeALC1KO   -77.66    1160.24  -0.067
##                                                 Pr(>|t|)    
## (Intercept)                                     5.33e-10 ***
## Experimentexp2                                  1.23e-05 ***
## Experimentexp3                                  0.000153 ***
## poly(Etoposide, 3)1                             3.81e-09 ***
## poly(Etoposide, 3)2                             0.565484    
## poly(Etoposide, 3)3                             0.916364    
## siRNAsiPARP1                                    2.96e-05 ***
## genotypeALC1KO                                  0.000107 ***
## poly(Etoposide, 3)1:siRNAsiPARP1                0.454676    
## poly(Etoposide, 3)2:siRNAsiPARP1                0.821318    
## poly(Etoposide, 3)3:siRNAsiPARP1                0.969780    
## poly(Etoposide, 3)1:genotypeALC1KO              0.687699    
## poly(Etoposide, 3)2:genotypeALC1KO              0.839682    
## poly(Etoposide, 3)3:genotypeALC1KO              0.912372    
## siRNAsiPARP1:genotypeALC1KO                     6.91e-05 ***
## poly(Etoposide, 3)1:siRNAsiPARP1:genotypeALC1KO 0.126864    
## poly(Etoposide, 3)2:siRNAsiPARP1:genotypeALC1KO 0.907400    
## poly(Etoposide, 3)3:siRNAsiPARP1:genotypeALC1KO 0.947078    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 290.1 on 30 degrees of freedom
## Multiple R-squared:  0.9175, Adjusted R-squared:  0.8707 
## F-statistic: 19.62 on 17 and 30 DF,  p-value: 8.983e-12

cat("AIC: ", AIC(fit9))

## AIC:  695.9863

simres <- simulateResiduals(fittedModel = fit9)
plot(simres)

fit10 <- lm(NormCounts ~ poly(Etoposide,3)*siRNA*genotype, data = dataset)
print(summary(fit10))

## 
## Call:
## lm(formula = NormCounts ~ poly(Etoposide, 3) * siRNA * genotype, 
##     data = dataset)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.44050 -0.08879 -0.00180  0.14256  0.31334 
## 
## Coefficients:
##                                                   Estimate Std. Error t value
## (Intercept)                                      1.000e+00  6.257e-02  15.981
## poly(Etoposide, 3)1                             -3.788e+00  4.335e-01  -8.738
## poly(Etoposide, 3)2                              1.515e-01  4.335e-01   0.349
## poly(Etoposide, 3)3                             -3.761e-02  4.335e-01  -0.087
## siRNAsiPARP1                                    -2.985e-16  8.849e-02   0.000
## genotypeALC1KO                                  -4.724e-16  8.849e-02   0.000
## poly(Etoposide, 3)1:siRNAsiPARP1                -2.529e+00  6.131e-01  -4.125
## poly(Etoposide, 3)2:siRNAsiPARP1                 6.843e-01  6.131e-01   1.116
## poly(Etoposide, 3)3:siRNAsiPARP1                 1.324e-01  6.131e-01   0.216
## poly(Etoposide, 3)1:genotypeALC1KO              -2.523e+00  6.131e-01  -4.115
## poly(Etoposide, 3)2:genotypeALC1KO               6.269e-01  6.131e-01   1.022
## poly(Etoposide, 3)3:genotypeALC1KO               2.795e-01  6.131e-01   0.456
## siRNAsiPARP1:genotypeALC1KO                      3.127e-16  1.251e-01   0.000
## poly(Etoposide, 3)1:siRNAsiPARP1:genotypeALC1KO  2.396e+00  8.671e-01   2.764
## poly(Etoposide, 3)2:siRNAsiPARP1:genotypeALC1KO -7.370e-01  8.671e-01  -0.850
## poly(Etoposide, 3)3:siRNAsiPARP1:genotypeALC1KO -3.503e-02  8.671e-01  -0.040
##                                                 Pr(>|t|)    
## (Intercept)                                      < 2e-16 ***
## poly(Etoposide, 3)1                             5.51e-10 ***
## poly(Etoposide, 3)2                             0.729023    
## poly(Etoposide, 3)3                             0.931406    
## siRNAsiPARP1                                    1.000000    
## genotypeALC1KO                                  1.000000    
## poly(Etoposide, 3)1:siRNAsiPARP1                0.000246 ***
## poly(Etoposide, 3)2:siRNAsiPARP1                0.272697    
## poly(Etoposide, 3)3:siRNAsiPARP1                0.830420    
## poly(Etoposide, 3)1:genotypeALC1KO              0.000253 ***
## poly(Etoposide, 3)2:genotypeALC1KO              0.314232    
## poly(Etoposide, 3)3:genotypeALC1KO              0.651548    
## siRNAsiPARP1:genotypeALC1KO                     1.000000    
## poly(Etoposide, 3)1:siRNAsiPARP1:genotypeALC1KO 0.009393 ** 
## poly(Etoposide, 3)2:siRNAsiPARP1:genotypeALC1KO 0.401620    
## poly(Etoposide, 3)3:siRNAsiPARP1:genotypeALC1KO 0.968022    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2168 on 32 degrees of freedom
## Multiple R-squared:  0.9581, Adjusted R-squared:  0.9385 
## F-statistic: 48.83 on 15 and 32 DF,  p-value: < 2.2e-16

cat("AIC: ", AIC(fit10))

## AIC:  3.977467

simres <- simulateResiduals(fittedModel = fit10)
plot(simres)

fit11 <- lm(NormCounts2 ~ poly(Etoposide,3)*siRNA*genotype, data = dataset)
print(summary(fit11))

## 
## Call:
## lm(formula = NormCounts2 ~ poly(Etoposide, 3) * siRNA * genotype, 
##     data = dataset)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.178665 -0.037267 -0.000769  0.058767  0.126061 
## 
## Coefficients:
##                                                 Estimate Std. Error t value
## (Intercept)                                      0.54138    0.02593  20.877
## poly(Etoposide, 3)1                             -2.05090    0.17966 -11.415
## poly(Etoposide, 3)2                              0.08202    0.17966   0.457
## poly(Etoposide, 3)3                             -0.02036    0.17966  -0.113
## siRNAsiPARP1                                    -0.13774    0.03667  -3.756
## genotypeALC1KO                                  -0.13578    0.03667  -3.702
## poly(Etoposide, 3)1:siRNAsiPARP1                -0.49900    0.25408  -1.964
## poly(Etoposide, 3)2:siRNAsiPARP1                 0.25533    0.25408   1.005
## poly(Etoposide, 3)3:siRNAsiPARP1                 0.05861    0.25408   0.231
## poly(Etoposide, 3)1:genotypeALC1KO              -0.50901    0.25408  -2.003
## poly(Etoposide, 3)2:genotypeALC1KO               0.23369    0.25408   0.920
## poly(Etoposide, 3)3:genotypeALC1KO               0.11847    0.25408   0.466
## siRNAsiPARP1:genotypeALC1KO                      0.13445    0.05186   2.592
## poly(Etoposide, 3)1:siRNAsiPARP1:genotypeALC1KO  0.46642    0.35932   1.298
## poly(Etoposide, 3)2:siRNAsiPARP1:genotypeALC1KO -0.27911    0.35932  -0.777
## poly(Etoposide, 3)3:siRNAsiPARP1:genotypeALC1KO -0.02025    0.35932  -0.056
##                                                 Pr(>|t|)    
## (Intercept)                                      < 2e-16 ***
## poly(Etoposide, 3)1                             8.08e-13 ***
## poly(Etoposide, 3)2                             0.651087    
## poly(Etoposide, 3)3                             0.910474    
## siRNAsiPARP1                                    0.000692 ***
## genotypeALC1KO                                  0.000802 ***
## poly(Etoposide, 3)1:siRNAsiPARP1                0.058274 .  
## poly(Etoposide, 3)2:siRNAsiPARP1                0.322482    
## poly(Etoposide, 3)3:siRNAsiPARP1                0.819023    
## poly(Etoposide, 3)1:genotypeALC1KO              0.053672 .  
## poly(Etoposide, 3)2:genotypeALC1KO              0.364596    
## poly(Etoposide, 3)3:genotypeALC1KO              0.644170    
## siRNAsiPARP1:genotypeALC1KO                     0.014246 *  
## poly(Etoposide, 3)1:siRNAsiPARP1:genotypeALC1KO 0.203548    
## poly(Etoposide, 3)2:siRNAsiPARP1:genotypeALC1KO 0.443001    
## poly(Etoposide, 3)3:siRNAsiPARP1:genotypeALC1KO 0.955418    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.08983 on 32 degrees of freedom
## Multiple R-squared:  0.9603, Adjusted R-squared:  0.9417 
## F-statistic: 51.63 on 15 and 32 DF,  p-value: < 2.2e-16

cat("AIC: ", AIC(fit11))

## AIC:  -80.5874

simres <- simulateResiduals(fittedModel = fit11)
plot(simres)

fit12 <- lmer(Counts ~ poly(Etoposide,3)*siRNA*genotype + (1|UID), data = dataset)
print(summary(fit12))

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Counts ~ poly(Etoposide, 3) * siRNA * genotype + (1 | UID)
##    Data: dataset
## 
## REML criterion at convergence: 459.6
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.17552 -0.31765 -0.02158  0.33740  2.36211 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  UID      (Intercept) 75654    275.1   
##  Residual             85359    292.2   
## Number of obs: 48, groups:  UID, 12
## 
## Fixed effects:
##                                                 Estimate Std. Error       df
## (Intercept)                                      1247.08     179.81     8.00
## poly(Etoposide, 3)1                             -4752.70     584.33    24.00
## poly(Etoposide, 3)2                               337.13     584.33    24.00
## poly(Etoposide, 3)3                               -61.44     584.33    24.00
## siRNAsiPARP1                                     -582.08     254.29     8.00
## genotypeALC1KO                                   -527.92     254.29     8.00
## poly(Etoposide, 3)1:siRNAsiPARP1                  621.44     826.36    24.00
## poly(Etoposide, 3)2:siRNAsiPARP1                  186.92     826.36    24.00
## poly(Etoposide, 3)3:siRNAsiPARP1                  -31.34     826.36    24.00
## poly(Etoposide, 3)1:genotypeALC1KO                333.00     826.36    24.00
## poly(Etoposide, 3)2:genotypeALC1KO                167.42     826.36    24.00
## poly(Etoposide, 3)3:genotypeALC1KO                 91.05     826.36    24.00
## siRNAsiPARP1:genotypeALC1KO                       772.75     359.62     8.00
## poly(Etoposide, 3)1:siRNAsiPARP1:genotypeALC1KO -1821.77    1168.65    24.00
## poly(Etoposide, 3)2:siRNAsiPARP1:genotypeALC1KO  -136.10    1168.65    24.00
## poly(Etoposide, 3)3:siRNAsiPARP1:genotypeALC1KO   -77.66    1168.65    24.00
##                                                 t value Pr(>|t|)    
## (Intercept)                                       6.936  0.00012 ***
## poly(Etoposide, 3)1                              -8.134 2.35e-08 ***
## poly(Etoposide, 3)2                               0.577  0.56934    
## poly(Etoposide, 3)3                              -0.105  0.91714    
## siRNAsiPARP1                                     -2.289  0.05134 .  
## genotypeALC1KO                                   -2.076  0.07155 .  
## poly(Etoposide, 3)1:siRNAsiPARP1                  0.752  0.45935    
## poly(Etoposide, 3)2:siRNAsiPARP1                  0.226  0.82296    
## poly(Etoposide, 3)3:siRNAsiPARP1                 -0.038  0.97006    
## poly(Etoposide, 3)1:genotypeALC1KO                0.403  0.69053    
## poly(Etoposide, 3)2:genotypeALC1KO                0.203  0.84116    
## poly(Etoposide, 3)3:genotypeALC1KO                0.110  0.91318    
## siRNAsiPARP1:genotypeALC1KO                       2.149  0.06389 .  
## poly(Etoposide, 3)1:siRNAsiPARP1:genotypeALC1KO  -1.559  0.13212    
## poly(Etoposide, 3)2:siRNAsiPARP1:genotypeALC1KO  -0.116  0.90826    
## poly(Etoposide, 3)3:siRNAsiPARP1:genotypeALC1KO  -0.066  0.94757    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

cat("AIC: ", AIC(fit12))

## AIC:  495.5766

simres <- simulateResiduals(fittedModel = fit12)
plot(simres)

Compare Results

ICtab(fit1,fit2,fit3,fit4,
      fit5,fit6,fit7,fit8,
      fit9,fit10,fit11,fit12,
      base=T)

##       AIC   dAIC  df
## fit7  -87.2   0.0 13
## fit3  -83.1   4.1 9 
## fit11 -80.6   6.6 17
## fit6   -2.6  84.6 13
## fit2    2.0  89.2 9 
## fit10   4.0  91.2 17
## fit12 495.6 582.8 18
## fit8  545.6 632.8 14
## fit4  608.7 695.9 10
## fit1  684.4 771.6 11
## fit5  688.1 775.3 15
## fit9  696.0 783.2 19

Final Result

fit <- fit7

output <- coef(summary(fit))
output <- output[grep("Etoposide", rownames(output)),]



rownames(output) <- gsub("poly\\(|, [1-3]\\)","", rownames(output) )
rownames(output) <- gsub("genotype",  paste0(" ",levels(dataset$genotype)[1], " vs. "), rownames(output))
rownames(output)[!(grepl("vs", rownames(output)))] <- paste(rownames(output)[!(grepl("vs", rownames(output)))], levels(dataset$genotype)[1],  sep = " in " )

rownames(output) <- gsub("siRNA",  paste0(" ",levels(dataset$siRNA)[1], " vs. "), rownames(output))
rownames(output)[!(grepl("vs.*vs| in ", rownames(output)))] <- paste(rownames(output)[!(grepl("vs.*vs| in ", rownames(output)))], levels(dataset$siRNA)[1],  sep = " in " )

rownames(output)[!(grepl("vs", rownames(output)))] <- paste(rownames(output)[!(grepl("vs", rownames(output)))], levels(dataset$siRNA)[1],  sep = " " )


# suggested result table
kable(output, row.names = T)

	Estimate	Std. Error	t value	Pr(>|t|)
Etoposide1 in WT siCtrl	-2.0508960	0.1718398	-11.9349267	0.0000000
Etoposide2 in WT siCtrl	0.0820225	0.1718398	0.4773193	0.6360200
Etoposide1: siCtrl vs. siPARP1 in WT	-0.4990002	0.2430182	-2.0533444	0.0473592
Etoposide2: siCtrl vs. siPARP1 in WT	0.2553265	0.2430182	1.0506474	0.3004225
Etoposide1: WT vs. ALC1KO in siCtrl	-0.5090070	0.2430182	-2.0945218	0.0433145
Etoposide2: WT vs. ALC1KO in siCtrl	0.2336853	0.2430182	0.9615957	0.3426674
Etoposide1: siCtrl vs. siPARP1: WT vs. ALC1KO	0.4664218	0.3436797	1.3571409	0.1831849
Etoposide2: siCtrl vs. siPARP1: WT vs. ALC1KO	-0.2791111	0.3436797	-0.8121256	0.4220574

write.table(output, file = "FigureS3_eto_Stats_Ref_WT.txt", quote = F, sep = "\t", row.names = T, col.names = NA)

# re-fit with ALC1KO reference
dataset$genotype <- relevel(dataset$genotype, ref = "ALC1KO")


fit <- lm(NormCounts2 ~ poly(Etoposide,2)*siRNA*genotype, data = dataset)

output <- coef(summary(fit))
output <- output[grep("Etoposide", rownames(output)),]


rownames(output) <- gsub("poly\\(|, [1-3]\\)","", rownames(output) )
rownames(output) <- gsub("genotype",  paste0(" ",levels(dataset$genotype)[1], " vs. "), rownames(output))
rownames(output)[!(grepl("vs", rownames(output)))] <- paste(rownames(output)[!(grepl("vs", rownames(output)))], levels(dataset$genotype)[1],  sep = " in " )

rownames(output) <- gsub("siRNA",  paste0(" ",levels(dataset$siRNA)[1], " vs. "), rownames(output))
rownames(output)[!(grepl("vs.*vs| in ", rownames(output)))] <- paste(rownames(output)[!(grepl("vs.*vs| in ", rownames(output)))], levels(dataset$siRNA)[1],  sep = " in " )

rownames(output)[!(grepl("vs", rownames(output)))] <- paste(rownames(output)[!(grepl("vs", rownames(output)))], levels(dataset$siRNA)[1],  sep = " " )


# suggested result table
kable(output, row.names = T)

	Estimate	Std. Error	t value	Pr(>|t|)
Etoposide1 in ALC1KO siCtrl	-2.5599030	0.1718398	-14.8970279	0.0000000
Etoposide2 in ALC1KO siCtrl	0.3157078	0.1718398	1.8372209	0.0744433
Etoposide1: siCtrl vs. siPARP1 in ALC1KO	-0.0325784	0.2430182	-0.1340573	0.8941039
Etoposide2: siCtrl vs. siPARP1 in ALC1KO	-0.0237846	0.2430182	-0.0978717	0.9225771
Etoposide1: ALC1KO vs. WT in siCtrl	0.5090070	0.2430182	2.0945218	0.0433145
Etoposide2: ALC1KO vs. WT in siCtrl	-0.2336853	0.2430182	-0.9615957	0.3426674
Etoposide1: siCtrl vs. siPARP1: ALC1KO vs. WT	-0.4664218	0.3436797	-1.3571409	0.1831849
Etoposide2: siCtrl vs. siPARP1: ALC1KO vs. WT	0.2791111	0.3436797	0.8121256	0.4220574

write.table(output, file = "FigureS3_eto_Stats_Ref_ALC1.txt", quote = F, sep = "\t", row.names = T, col.names = NA)

Anova

fit7a <- lm(NormCounts2 ~ poly(Etoposide,2)*siRNA*genotype, data = dataset)
fit7b <- lm(NormCounts2 ~ poly(Etoposide,2)*siRNA+genotype, data = dataset)

# anova table
anova(fit7a, fit7b)

## Analysis of Variance Table
## 
## Model 1: NormCounts2 ~ poly(Etoposide, 2) * siRNA * genotype
## Model 2: NormCounts2 ~ poly(Etoposide, 2) * siRNA + genotype
##   Res.Df     RSS Df Sum of Sq      F Pr(>F)  
## 1     36 0.26576                             
## 2     41 0.35969 -5 -0.093931 2.5448 0.0452 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

fit7c <- lm(NormCounts2 ~ poly(Etoposide,2)*genotype*siRNA, data = dataset)
fit7d <- lm(NormCounts2 ~ poly(Etoposide,2)*genotype+siRNA, data = dataset)

# anova table
anova(fit7c, fit7d)

## Analysis of Variance Table
## 
## Model 1: NormCounts2 ~ poly(Etoposide, 2) * genotype * siRNA
## Model 2: NormCounts2 ~ poly(Etoposide, 2) * genotype + siRNA
##   Res.Df     RSS Df Sum of Sq      F Pr(>F)  
## 1     36 0.26576                             
## 2     41 0.35947 -5 -0.093711 2.5388 0.0456 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1